1. No category

Downloaded from rstb.royalsocietypublishing.org on September 7, 2012
What is comparable in comparative cognition?
Lars Chittka, Stephen J. Rossiter, Peter Skorupski and Chrisantha Fernando
Phil. Trans. R. Soc. B 2012 367, 2677-2685
doi: 10.1098/rstb.2012.0215
Supplementary data
"Audio supplement"
http://rstb.royalsocietypublishing.org/content/suppl/2012/08/28/rstb.2012.0215.DC2.ht
ml
References
This article cites 90 articles, 28 of which can be accessed free
http://rstb.royalsocietypublishing.org/content/367/1603/2677.full.html#ref-list-1
Article cited in:
http://rstb.royalsocietypublishing.org/content/367/1603/2677.full.html#related-urls
Subject collections
Articles on similar topics can be found in the following collections
behaviour (390 articles)
cognition (249 articles)
evolution (533 articles)
neuroscience (300 articles)
Email alerting service
Receive free email alerts when new articles cite this article - sign up in the box at the top
right-hand corner of the article or click here
To subscribe to Phil. Trans. R. Soc. B go to: http://rstb.royalsocietypublishing.org/subscriptions
Downloaded from rstb.royalsocietypublishing.org on September 7, 2012
Phil. Trans. R. Soc. B (2012) 367, 2677–2685
doi:10.1098/rstb.2012.0215
Review
What is comparable in comparative
cognition?
Lars Chittka1,*, Stephen J. Rossiter1, Peter Skorupski1
and Chrisantha Fernando2
1
Biological and Experimental Psychology Group, School of Biological and Chemical Sciences, and
2
School of Electronic Engineering and Computer Science, Queen Mary University of London,
London E1 4NS, UK
To understand how complex, or ‘advanced’ various forms of cognition are, and to compare them
between species for evolutionary studies, we need to understand the diversity of neural– computational mechanisms that may be involved, and to identify the genetic changes that are necessary
to mediate changes in cognitive functions. The same overt cognitive capacity might be mediated
by entirely different neural circuitries in different species, with a many-to-one mapping between behavioural routines, computations and their neural implementations. Comparative behavioural
research needs to be complemented with a bottom-up approach in which neurobiological and
molecular-genetic analyses allow pinpointing of underlying neural and genetic bases that constrain
cognitive variation. Often, only very minor differences in circuitry might be needed to generate
major shifts in cognitive functions and the possibility that cognitive traits arise by convergence or
parallel evolution needs to be taken seriously. Hereditary variation in cognitive traits between individuals of a species might be extensive, and selection experiments on cognitive traits might be a
useful avenue to explore how rapidly changes in cognitive abilities occur in the face of pertinent
selection pressures.
Keywords: brain; convergent evolution; homology; intelligence; neural circuits
1. INTRODUCTION
The central goal of comparative cognition is, of course,
to find out how cognition evolves. To this end, standard
evolutionary tools are used—one maps traits onto
established phylogenetic trees, and in this way infers
homology, evolutionary ancestry of cognitive traits or
evolutionary convergence [1]. We argue that many
such studies in the past have applied a relatively restrictive top-down approach, where researchers sometimes
set out to discover human-like behaviours in animals
[2,3]. This approach is prone to terminological ambiguities, because terms taken from the domain of human
experience often invoke more complex connotations
than the restricted criteria used to test the phenomena
in an experimental setting with animals would permit.
It also bears the risk of circularities as a consequence
of selecting and shaping definitions and test criteria
[4]. Finally, a narrow top-down approach may be vulnerable to biases, such as to predominantly report
positive results or to mainly investigate ‘clever’ animals
[5]. A mechanistic, bottom-up approach is needed,
with a focus on exploring the neural underpinnings of
cognitive features, and the genes that control them
[6–8]. This will be useful not only to quantify and
* Author for correspondence ([email protected]).
One contribution of 14 to a Discussion Meeting Issue ‘Animal
minds: from computation to evolution’.
compare cognitive complexity, but also to explore the
evolutionary background of cognitive traits. Contrary
to the popular approach to dissect ‘why’ questions
in biology into proximate and ultimate perspectives,
we hold that questions about the evolution of behaviour and cognition are difficult to answer without an
explanation of the mechanisms that are the basis of
variation, not just the functions that are under selection.
In exploring whether non-human animals have consciousness, culture, theory of mind, for example, and
to explore the evolutionary roots of these phenomena,
many studies actually focus on whether behavioural
feats conform to certain definitions, rather than
explore whether these are based on comparable evolutionary features [7,9]. Concepts and terms are often
borrowed from studies on human psychology, and then
applied to other animals [2]. In the process, definitions
are sometimes adjusted so that they can be applied to
animals in meaningful ways—this is fine in principle,
but the more permissive the definition, the more species
will qualify, and the more examples of functional convergence (or indeed homology) we will find; in other words,
the identification of evolutionary patterns in cognition
will hinge, to some extent, on semantics, rather than
actual biological traits. Given the many controversies
about how to diagnose and define cognitive performance,
this is surely an undesirable scenario.
In addition, one might argue that some of the most
exciting discoveries about unique behaviour patterns
2677
This journal is q 2012 The Royal Society
Downloaded from rstb.royalsocietypublishing.org on September 7, 2012
2678
L. Chittka et al.
Review. Cognition evolution
would never have been made, had a strictly top-down,
human-anchored approach been used throughout.
Would Frisch [10] ever have discovered the bee
‘dance language’ had he deliberately set out to find a
form of ‘language’ in the animal kingdom? Probably
not—and in fact the question of definition—whether
or not this symbolic communication warrants the
term ‘dance’ or in fact a ‘language’—is perhaps secondary to the fascination with the uniqueness of this
communication system.
Even if we can agree on the definition of cognitive
traits, the question remains whether behavioural performance can be compared in meaningful ways
across species without a consideration of underlying
mechanisms. For example, an analysis of colour learning speed in 11 animal species found honeybees to be
the fastest at reaching a criterion, followed by fishes,
then birds, and the slowest were human infants [11].
Curiously, this result, which runs counter to the
accepted scala naturae, was then used to argue that
learning speed is not a useful measure of intelligence!
There may be good reasons not to equate learning
speed with intelligence, but the fact that humans do
not top the chart should not be one of them. The
reason that honeybees perform well on colour learning
tasks is that evolution has prepared them to memorize
floral cues [12,13]. This complication—that a given
task will often be closer to some species’ natural
biology than to some other species’—has led to the recommendation that cognition should be explored in tasks
that are removed from those typically encountered in the
animals’ natural setting [14]. But, it appears almost
impossible to rule out entirely that some animals will
be better prepared to deal with certain laboratory tasks
because they might be related, in subtle ways, to naturally relevant challenges. To compensate for such
complications, some researchers have suggested that
different taxa require adjustments in experimental techniques [15,16]. However, it is sometimes unclear
whether this improves comparability of performance
across species or eliminates it altogether.
An added complication is that negative results in
comparative cognition are sometimes hard to interpret—did the animal fail the test because it really
lacked an ability, or because inappropriate methods
were employed [7]? There are many examples where
negative results have been initially interpreted to mean
that, for example, apes do not display imitation,
theory of mind, or understand gravity, where subsequent work showed that, with adjusted procedures,
animal subjects pass the grade after all [7,17]. One
possible solution is to gradually increase task complexity until performance breaks down [18], but just as with
our discussion of definitions above, there is some
danger that we might adjust procedures or behavioural
criteria until we find the desired phenomenon.
Finally, a complication in mapping overt behavioural abilities onto phylogenetic trees is that the same (or
similar) sensory or cognitive feats might be generated
by entirely different mechanisms that have arisen by
convergent evolution [19]. On occasion, these are
clearly identifiable as instances of convergence when
abilities have emerged in highly distinct lineages,
such as echolocation in bats and dolphins [20,21] or
Phil. Trans. R. Soc. B (2012)
flexible tool use in primates and corvids [22]. Yet, distinguishing homology from convergence is sometimes
difficult without knowledge of the underlying mechanisms. Electrolocation in weakly electric fish arose
independently in the South American and African
lineages, where different areas of the brain mediate
the analysis of electro-sensory input [23]. Examples
of parallel evolution, where behavioural abilities arise
independently from homologous structures or genes
are also widespread [19,21], and these cases illustrate
the difficulty in deducing homology or convergence
from mere behavioural abilities even more strongly.
2. COMPARISONS OF GROSS NEUROANATOMY
The complications with using either brain size or gross
anatomy as correlates of cognitive capacity are clear
[4,24], so we will not dissect them here in detail. Without
an exploration of internal neuronal wiring patterns, such
analyses are unhelpful for comparative studies. There
are now a number of very instructive studies where
comparisons of cognitive abilities in different species
are complemented with investigations of the underlying
neural circuitry [7], where neurobiological work has
indeed helped resolve controversies whether certain abilities can be said to exist in non-humans. For example,
in studies on face recognition, imitation, prosocial behaviour and empathy, it appears that homologous
cortical structures are recruited in humans and various
primates when engaged in tasks related to these traits.
Such results inspire some confidence in the fact that
these behavioural phenomena (albeit tested somewhat
differently in humans and non-humans) are comparable
and homologous [7].
Features of gross brain anatomy can be well mapped
onto phylogenies of various taxa [25,26], suggesting
some phylogenetic constraints on brain evolvability
[19]. On the other hand, it is not clear to what
extent this constrains the evolution of cognitive
capacity in the face of relevant selective pressures, or
only insofar as evolution tends to proceed along similar
paths with similar starting points when selection pressures are similar. For example, New World Cebus
monkeys and Old World macaques have both evolved
a high level of dexterity that is accompanied by parallel
enlargements of homologous cortical areas involved in
proprioception, motor planning and visually guided
hand movements [27]. Song learning in birds has
evolved at least twice by homologous anterior forebrain regions convergently assuming similar functions
[19]. Similarly, the independent evolution of highduty cycle echolocation in unrelated lineages of Old
World and New World bats—which enhances the ability
to detect fluttering insects [28]—correlates with specializations in the cochlea and higher auditory centres [29].
Such examples suggest that evolution repeatedly
proceeds along similar paths, perhaps where exapted
circuits are well-preconfigured to produce novel
behaviours when selection favours them.
Many impressive behavioural/cognitive capacities,
while they clearly must have a neural substrate, are
not readily detectable in terms of gross neuroanatomy.
For example, one of the most impressive discoveries
in insect social cognition in recent times was the
Downloaded from rstb.royalsocietypublishing.org on September 7, 2012
Review. Cognition evolution
discovery of individual face recognition in Polistine
wasps, and its role in social hierarchies [30]. Nonetheless, no discernable differences were found in the
neuropiles of the visual systems of these remarkable
wasps compared with related species in which face recognition does not occur [31]. Likewise, in an attempt
to identify a neural correlate of the honeybee ‘dance
language’, no ‘dance-specific’ sensory projections
were found in the brains of honeybees when compared
with other species that lack the dance [32]. The
human brain, for all its obvious differences in cognitive
output compared to other primates, appears, at least, in
terms of coarse organization, in many respects to be a
scaled-up version of the primate brain [7,33]. While
there are certainly differences at the circuitry level, cortical modules once thought to be engaged in uniquely
human functions (e.g. Broca’s and Wernicke’s areas)
have clear homologues in other primates [34,35]. This
emphasizes that even seemingly major evolutionary
innovations relevant to behaviour and cognition might
be generated by relatively small adjustments in neural
circuitry that might be hard to detect by coarse-voxelled
imaging studies or gross neuroanatomy.
3. COMPARISONS AT THE CIRCUITRY LEVEL
While we do not know the detailed neuron-to-neuron
circuitry for any advanced cognitive function, it is
clear that this has to be a desideratum to make meaningful comparisons between species. There are several
lines of evidence that cognitive operations can be
performed within fairly small circuits [36] and that
therefore evolutionary shifts in cognitive capacity
could be mediated by changes at the microcircuit
level. One is that many ‘advanced’ cognitive capacities
have recently been discovered in small brained animals
such as insects (see [4,37,38] for reviews), and minimum circuitry models show that many such abilities
could be mediated by very small neuron numbers
and connections (see below). In addition, the widespread nature of the basic building blocks for
cognition throughout the animal kingdom indicates
that various forms of behavioural plasticity were, at
least, in principle evolvable in some of the earliest animals. Almost all of the molecular components of nerve
cells, i.e. ion channels, pumps, exchangers, neurotransmitters and G-protein-coupled receptors—are
likely to have been present in the common ancestor
of vertebrates and invertebrates [4]. Presumably as a
result of convergent evolution based on these building
blocks, connections between neurons display similar
plasticity in vertebrates and invertebrates, including
short-term, spike-timing-dependent and long-term
plasticity [39,40]. Basic circuit architecture is similar
in insects and vertebrates, for example lateral inhibition, feed-forward and feedback excitation and
inhibition, and presynaptic inhibition [41,42].
The many cases of parallel evolution in the sensory
and motor systems further illustrate the readiness with
which features of nervous systems (and the genes that
control them) can often respond to pertinent selective
pressure based on exaptations (i.e. the reuse of previously evolved adaptations for an entirely different
function over evolutionary time-scales). For example,
Phil. Trans. R. Soc. B (2012)
L. Chittka et al.
2679
red sensitivity has evolved several times in insect
colour vision [43], and in Lepidoptera this has repeatedly occurred by gene duplication of a ‘green’ sensitive
opsin and subsequent spectral sensitivity shift to
longer wavelengths by the same amino acid substitutions [44]. Numerous vertebrate lineages also show
adaptive duplications or losses of visual pigment
genes, as well as more subtle spectral tuning of their
protein products via (sometimes parallel) amino acid
substitutions [45–47]. In mammals, at least, the nervous systems appears well-preconfigured to instantly
add a novel sensory dimension to its perception without
evolutionary lag-time to adjust postreceptor neural circuitry [48], perhaps by means of general purpose
decorrelation mechanisms [49]. Invertebrates seem no
less flexible to respond adaptively to completely novel
sensory-motor contingencies within a short period of
time [50].
The parallel or convergent acquisition of neural circuits for performing different cognitive tasks is perhaps
not surprising, because such changes could be brought
about by a multitude of permutations of the existing
molecular hardware [51]. In comparison, known
examples of convergent evolution at the primary
DNA sequence level are scarce, though this situation
could soon change with the increasing rate at which
new genomes are being sequenced. An instructive
case is the parallel evolution of ultrasonic hearing in
cetaceans and bats. The gene encoding the motor
protein prestin (which confers high-frequency sensitivity in the outer hair cells in the cochlea) has
undergone several identical amino acid replacements
[20,21] as well as bursts of molecular adaptation in
unrelated lineages of echolocating bats [52], and also
between bats and toothed whales [20,21]. Subsequent
discoveries of parallel sequence evolution in other
genes expressed in the cochlear hair cells of bats and
cetaceans suggest that the evolution of echolocation
has involved convergent changes in multiple loci [53].
The potential for small genetic changes to have profound impacts on neuro-computational properties is
perhaps best illustrated by studies of genetic mutation
in humans and other taxa. For example, deleterious
mutations in the human gene FOXP2 are associated
with deficits in language and speech development,
probably caused by alterations to the cortical and
sub-cortical neural circuitry [54,55], whereas the
occurrence of just two adaptive amino acid replacements on the branch leading to humans since our
split from chimpanzees has been implicated in the
evolution of language itself [56]. How such drastic
developmental and/or evolutionary effects could be
brought about by evidently small genetic changes is
better understood in the light of recent findings showing that the encoded transcription factor FOXP2
regulates a range of downstream genes in neural
tissue [57], and that the humanized protein targets
particular regions in the brains of mice [58]. Results
from FoxP2 in non-humans, including observations
of spatio-temporal expression patterns in the brains
of song learning zebra finches [59] and impaired
song imitation in this species following knockdown
[60] suggest a more general role in vocal learning
(reviewed by Bolhuis et al. [61]). Accelerated rates of
Downloaded from rstb.royalsocietypublishing.org on September 7, 2012
2680
L. Chittka et al.
Review. Cognition evolution
FoxP2 sequence evolution [62] coupled with parallel
expression in auditory nuclei [63] have also been
observed in divergent lineages of another group of
vocal learners—the echolocating bats—though it is
unclear whether these data relate to echolocation
rather than learning per se.
On the other hand, and further demonstrating the
tremendous potential of nervous systems to rewire
themselves over evolutionary time, there are impressive
examples of homologous neurons or circuits used in
divergent functions, e.g. in some sand crabs where
motor neurons became sensory stretch receptors [64].
In conclusion, the understanding of the evolution of
behavioural–cognitive traits is greatly facilitated by considering the underlying mechanisms. Often, relatively
minor alterations in circuitry might be necessary to
produce novel behavioural capacities [4,19,65].
4. NATURAL VARIATION OF COGNITION WITHIN
SPECIES AND SELECTION EXPERIMENTS
In questioning how easily cognition might evolve
under pertinent selective regimes, it is of course crucial
to explore the heritable variation for cognitive traits
within species [66,67]. While many workers have treated individual variation in cognitive performance as
noise, such variation is the raw material for evolution
and therefore deserves close attention. In bees, for
example, all aspects of learning behaviour and cognition are individually variable, and often relate to
colony membership, suggesting genetic underpinnings
[68– 71]. Learning ability is highly relevant to
biological fitness under natural conditions [72], and
selection experiments on bees [73] and other insects
[74] reveal that learning speed responds exceptionally
well to selection. Interestingly, in work on the colour
vision system of guppies, it has been experimentally
shown that populations of the same species might
respond to the same selective pressure by adjusting different mechanisms to mediate the same (or similar)
behavioural abilities [75].
So far, selection experiments have often focused on
associative learning, not the more advanced cognitive
feats [37], perhaps because these are much less easy
to test for large numbers of individuals. There is nonetheless little doubt that more advanced cognitive
faculties also display individual variation in many if
not most animal species; Darwin, for example,
described individual variation of attention in monkeys
[76]. In humans, the study of the genetic basis of individual variation in cognitive capacity is a rapidly
expanding field [77], and indeed twin, family and
adoption studies reveal that all human cognitive abilities have heritable components [78]. For individual
differences in working memory, episodic memory,
novelty seeking and reward processing, there is now
information on some of the molecular–genetic underpinnings, and unsurprisingly such variation often
involves multiple genes [77]. More data on nonhuman animals and traits naturally under selection are
needed, but the overall message from this section is
that because individual, heritable variations in cognitive
traits are presumably widespread, it is predicted that
populations should often respond rapidly to selective
Phil. Trans. R. Soc. B (2012)
pressure for certain cognitive feats. The absence of a
given cognitive ability in an animal might not necessarily indicate constraints of the evolvability of the
neural hardware, but instead the absence of a relevant
selection pressure. There is also a caveat here: the general premise to infer homology from the observation
that related species share the same cognitive traits
(e.g. humans and chimpanzees) is of course based on
the idea that cognitive traits are as ‘difficult’ to evolve
as morphological ones. Humans and chimpanzees, for
example, have evolved separately for many tens of thousands of generations, i.e. extensive time to evolve
behavioural capacities by parallel or convergent evolution based on shared selective pressures, given that
selection experiments for behavioural traits can
often produce dramatic results within a dozen or so
generations (although of course selection experiments
so far have not produced true qualitative shifts in
cognitive capacity).
5. WHAT IS COGNITIVE COMPLEXITY AND HOW
DOES IT EVOLVE?
In asking how complex, or ‘advanced’ various forms of
cognition are, and to compare them between animal
species, there is often a tendency to rank various types
of plasticity from simple (e.g. habituation, sensitization)
through intermediate (from classical conditioning to
rule learning) to advanced (human-like, e.g. theory
of mind, metacognition, consciousness). The implication is often that the more ‘advanced’ forms are
more difficult to evolve, and might require large
brains. However, we have to ask by what criteria we
might assess ‘complexity’—what exactly makes one
form of cognition more advanced than another?
A common assumption of cognitive scientists and
behavioural biologists is that perception involves the
ordering of sensory information into internal representations, and that cognition involves the manipulation
of representations (derived from both perception
and memory). If this is so, then defining cognitive complexity could, in principle, be quite straightforward.
Organisms construct internal (cognitive) models of the
world and cognitive complexity is just the complexity
of these models. If we further assume that such
models are computational, then cognitive complexity
is quantifiable, because there are mathematical ways of
describing complexity. The problem with this abstraction is that in no case do we yet have a complete
computational simulation of an organism’s model of
the world (i.e. of a real brain). However, even if we
did, another question would then arise: if such cognitive
models of the world vary in complexity, could they also
vary in efficiency? Could a less complex model do the
job just as well, or better [79]?
For example, would an internal spatial representation (cognitive map) of an animal’s environment
represent greater complexity than a system for guiding
navigational behaviour based on path integration [80]?
One possibility to quantify the complexity of a cognitive feat might be to use the minimal cognition
approach. It has recently been shown, for example,
that behaviour commonly interpreted in terms of
map-like guidance can be generated by a very simple
Downloaded from rstb.royalsocietypublishing.org on September 7, 2012
Review. Cognition evolution
simulation containing no such explicit internal representation [81]. What are the minimal components
needed to perform a given function? Given the absence
of a complete simulation (a model of the brain’s
model), many computational studies have opted for
the ‘minimal cognition’ approach. For example, dynamical systems studies combined with artificial evolution of
algorithms for control of real or simulated agents
(evolutionary robotics or evolutionary computation,
respectively) suggest that the number of neurons
required to simulate a minimal cognitive feat is exceedingly small. Continuous-time recurrent neural networks
can be artificially evolved to undertake perceptual categorization of falling ‘triangles’ and ‘squares’ using active
vision with only eight neurons [82]. Relational categorization of concepts such as larger or smaller can be
achieved using five neurons [83]. By using models of
synaptic plasticity and neuromodulation [84], and
again using in silico artificial evolution to find efficient
circuits, it was possible to discover eight neuron
reinforcement learning circuits that can learn the quality
of reward associated with a resource and exhibit
probability matching and risk-aversion phenomena
predicted by optimal foraging theory [85]. Further
work has examined predator–prey coevolution using
similarly small neural networks to control behaviour,
and even examined small neural circuits for altruism
and cooperation [86]. While the tasks solved by these
networks are undoubtedly simplifications of those
encountered by real animals in nature, the overall message is that seemingly ‘advanced’ cognitive capacities can
often be mediated by relatively simple circuits.
Evolutionary modelling is useful for mathematically formalizing thought experiments to identify
simple solutions capable of explaining complex behaviours for which one might have otherwise thought
that more complex representations were required.
Using in silico evolution, we can produce small circuit
designs that could not have been easily generated
by human design principles, demonstrating that
evolution could have had access to the variation
required to solve apparently complex cognitive problems using compact ‘tricks’. Conclusions about the
evolvability of real cognition are still tentative, however, because such simple model solutions are found
within a relatively constrained search space, where
the network parameters specified in the ‘genome’
scale in a polynomial fashion with network size
(unlike real genomes). Moreover, the search space
of real cognitive evolution is constrained by the
structure of reality (i.e. previous adaptations) unlike
in most in silico experiments, where the solutions
are generally independent of the prior evolution of
other adaptations.
Finally, we might note that the minimal cognition
approach does not readily provide any principled way
of quantifying cognitive complexity; the real problem
may lie in understanding the hierarchical structure of
animals’ cognitive models of the world [87], which, in
principle, is excluded from studies of minimal cognition. Nevertheless, an important conclusion is that the
minimum number of neurons necessary to perform a
variety of cognitive tasks is typically very small.
Ultimately, this may allow us to itemize cognitive
Phil. Trans. R. Soc. B (2012)
L. Chittka et al.
2681
‘toolkits’ of functions [22], whereby the more versatile
the toolkit the greater the cognitive complexity.
6. DATA-DRIVEN APPROACHES TO BEHAVIOUR
AND COGNITION
A recent development is to turn away from the widespread expectation-driven (top-down, deductive)
approach to the analysis of behaviour and cognition,
and to classify behaviour by automated procedures
so they can be objectively measured and compared
using what Branson et al. [88] have called an ‘ethomics’ approach. New technologies in motion capture
and video analyses make an entirely hypotheses-free
approach to the analysis of behaviour feasible [88– 90].
For the future, it will be necessary to develop largescale automated analytical tools to effectively mine
the data collected with the aim of finding prototypical and atypical behaviours. To discover prototypical
activity models, a useful approach is to implement
procedures for clustering accumulated trajectory data
into representative patterns [90]. We will ultimately
have to break down behavioural sequences into their
smallest identifiable units, and quantify them in terms
of trajectories of body parts, their speed, distance,
acceleration, deceleration, etc., thereby identifying behavioural prototypes [90–92] that can subsequently
be strung together into various sequences to reconstruct
meaningful natural behaviour. To encode the variability
of the behavioural repertoires, it will be necessary to
develop a dedicated alphabet and syntax of movements
that can be effectively compared between individuals
and species. An automated ‘data mining’ approach
will also facilitate the discovery of new behaviour patterns that have so far escaped the attention of human
observers. In this bottom-up approach, we are less
constrained by one particular concept, but we are also
much freer to observe the peculiarities of the animals’
behaviours. Animal cognition and behaviour, in all
their diversity, are unlikely to be wholly describable
by the concepts that are derived from just one, very
unusual species, Homo sapiens. By looking at many
more animal species, under more test conditions, and
by testing many, and smaller, i.e. more basic criteria
(cognitive or behavioural units), we probably could
build cognitive constructs that are more independent
from the human world and therefore, ultimately, more
informative [2].
It is clear that such an approach cannot stand on its
own, and needs to be coupled with experimental
manipulation, which in turn needs to be hypothesisdriven. Learnt behaviours can be tested in meaningful
ways only by exposing animals to controlled stimuli,
and these cannot be generated in a hypothesis-free
manner. Additionally, learning may generate a practically infinite number of behavioural routines that
might be difficult to pick up in clustering algorithms—consider, for example, that the diversity of
flower handling procedures required of a generalist
pollinator to extract nectar or pollen is presumably as
great as the number of floral morphologies [93].
Nonetheless, these might consist of small-scale hardwired motor routines strung together in flexible ways
[94]. Again, we would reveal this only by breaking
Downloaded from rstb.royalsocietypublishing.org on September 7, 2012
2682
L. Chittka et al.
Review. Cognition evolution
down behaviour into small components, using
methods as described earlier.
While the fully automated, bias-free analysis of behaviour is now becoming increasingly feasible, this is
less straightforward with cognitive processes that
might or might not have observable outcomes. There
is no question that hypothesis-driven behavioural
experiments are a necessary ingredient of studying
animal cognition (although the hypotheses might
more suitably be driven by the motivation for scientific
enquiry rather than for confirming that animals are
clever). Nonetheless, there is plenty of room for
more bias-free approaches in the study of cognitive
approaches as well.
For example, multi-electrode recordings provided
profound insights into how the brain stores and
organizes memories in the rodent hippocampus
[95,96], and the number of cells recorded from simultaneously (more than 100 in some cases) is impressive
and contributed substantially to the understanding of
the neural ensemble code of the mammalian hippocampus. Although the settings in which the data are
recorded are of course hypothesis-based, their subsequent analysis is largely data-driven, in that
information contained about individual neuronal
firing patterns, and their propagation and integration,
needs to be extracted from the raw data by data
mining processes. A recent neuroanatomical study, for
example, succeeded in identifying and mapping
16 per cent of the approximately 100 000 neurons of
the Drosophila brain [97], illustrating just how far we
have progressed in the direction of understanding the
circuitry of insects’ brains. Other recent developments
make the imaging of entire neural circuits, and possibly
whole brains at micrometre resolution, increasingly
feasible [98,99]. It is quite conceivable that as such
technologies develop we will discover neural–computational solutions to cognitive tasks in non-human
animals quite unlike those in humans (even where the
behavioural outcomes might be similar). We propose
that such bottom-up approaches reduce the importance
of introspection for the ‘verification’ of cognitive constructs; they could therefore contribute to create a
more objective, more independent, and less arbitrary
methodology for comparative cognition.
Neural network modelling can provide useful pointers for neuroscientists to concentrate their efforts on
what to explore, and here, too, a more open-ended
approach is needed. Often, modellers search for a
single computational solution that best explains an
empirically determined phenomenon. An alternative
approach would be an emphasis on diversity—for
example, for a given cognitive problem, how many
neural solutions might be generated, and how does
this depend on the number of available neurons (and
their connections)? Circuit functionality can be
assessed by varying all parameters in discrete steps
and keeping a complete record of simulation results,
including the output for each varied parameter value
under all combinations of parameter settings, as has
been done in simulations to derive optimal colour
coding systems where thousands of possible solutions
were fully analysed [100]. A good example of a
bottom-up approach in computational neuroscience
Phil. Trans. R. Soc. B (2012)
is a model of the fly mushroom body, which, rather
than being built to mimic known behavioural
affordances, instead implemented all neurobiological
information available into a comprehensive circuitry
model to subsequently ask which forms of learning
might be produced by such circuitry [101].
The remarkable result was that, while the model
predicted several known forms of elemental and nonelemental learning in Drosophila, it also predicted
some forms of learning that had not yet been behaviourally confirmed [101]. This sort of modelling
might indeed reveal previously undescribed forms of
information processing that could subsequently be
tested by behavioural experiments.
Finally, bottom-up approaches for comparing cognitive abilities among individuals, populations or
taxa must consider the sequences of the underlying
candidate genes, as well as their protein products,
expression patterns and molecular targets. The very
recent development of new sequencing technologies
has revolutionized the rate at which new genome
data are being generated and analysed [102], so allowing increasing numbers of taxa to be studied at depths
previously possible for just a tiny number of model
organisms. The associated boom in studies of phylogenomics, population genomics and genome-wide
associations all present unprecedented opportunities
for gene discovery, allowing us to distinguish between
ancestral and derived character states for thousands of
loci [103,104]. Combining such gene sequence data
with transgenic and recombinant technologies—
which are already being applied to studies of cognition
[58,105]—offers a promising and powerful means of
dissecting the commonalities and differences in
neuro-computational and cognitive processing in a
comparative framework.
7. SUMMARY
There is an urgent need to understand animal ‘intelligence’ not just by apparent levels of task complexity
and the extent to which human observers are
impressed by animal cleverness, but in neural-computational terms: how many neurons, with what
connections, and what computational operations are
necessary to solve a given task? Without such information, the principal challenge of comparative
cognition will remain difficult to overcome: there is
limited information in mapping behavioural phenomena onto a phylogeny to infer homology or
convergence, so long as the underpinning mechanisms
are unknown. The quest for understanding the precise
neuron-to-neuron connectivity underlying cognitive
processes can be addressed both from an empirical
angle, as well as from a modelling perspective. To
understand how (and how easily) cognition and behaviour evolve, we must understand the neural circuitry
that mediates a given cognitive trait and the genes
(and their regulation) involved in its expression.
REFERENCES
1 Maclean, E. L. et al. 2012 How does cognition evolve?
Phylogenetic comparative psychology. Anim. Cogn. 15,
223 –238. (doi:10.1007/s10071-10011-10448-10078)
Downloaded from rstb.royalsocietypublishing.org on September 7, 2012
Review. Cognition evolution
2 Do¨ring, T. F. & Chittka, L. 2011 How human are insects,
and does it matter? Formosan Entomol. 31, 85–99.
3 Heyes, C. 2012 Simple minds: a qualified defence
of associative learning. Phil. Trans. R. Soc. B 367,
2695–2703. (doi:10.1098/rstb.2012.0217)
4 Chittka, L. & Niven, J. 2009 Are bigger brains better?
Curr. Biol. 19, R995 –R1008. (doi:10.1016/j.cub.2009.
08.023)
5 Shettleworth, S. J. 2010 Clever animals and killjoy
explanations in comparative psychology. Trends Cogn.
Sci. 14, 477 –481. (doi:10.1016/j.tics.2010.07.002)
6 Willems, R. M. 2011 Re-appreciating the why of cognition: 35 years after Marr and Poggio. Front. Psychol. 2,
244. (doi:10.3389/fpsyg.2011.00244)
7 de Waal, F. B. M. & Ferrari, P. F. 2010 Towards a
bottom-up perspective on animal and human cognition.
Trends Cogn. Sci. 14, 201 –207. (doi:10.1016/j.tics.2010.
03.003)
8 Chittka, L. & Skorupski, P. 2011 Information processing in miniature brains. Proc. R. Soc. B 278,
885 –888. (doi:10.1098/rspb.2010.2699)
9 Bolhuis, J. J. & Wynne, C. D. L. 2009 Can evolution
explain how minds work? Nature 458, 832–833.
(doi:10.1038/458832a)
10 Frisch, K. v. 1967 The dance language and orientation of
bees. Cambridge, MA: Harvard University Press.
11 Pearce, J. M. 2008 Animal learning and cognition, 3rd
edn. New York, NY: Psychology Press.
12 Arnold, S. E. J., Savolainen, V. & Chittka, L. 2009
Flower colours along an alpine altitude gradient, seen
through the eyes of fly and bee pollinators. ArthropodPlant Interactions 3, 27–43. (doi:10.1007/s11829-0099056-9)
13 Chittka, L. & Menzel, R. 1992 The evolutionary adaptation of flower colors and the insect pollinators’ color
vision systems. J. Comp. Physiol. A 171, 171– 181.
14 Roth, G. & Dicke, U. 2005 Evolution of the brain and
intelligence. Trends Cogn. Sci. 9, 250– 257. (doi:10.
1016/j.tics.2005.03.005)
15 Bitterman, M. E. 1975 The comparative analysis of
learning. Science 188, 699 –709. (doi:10.1126/science.
188.4189.699)
16 Shettleworth, S. J. 2010 Cognition, evolution and behavior. Oxford, UK: Oxford University Press.
17 Seed, A., Seddon, E., Greene, B. & Call, J. 2012 Chimpanzee ‘folk physics’: bringing failures into focus. Phil.
Trans. R. Soc. B 367, 2743–2752. (doi:10.1098/rstb.
2012.0222)
18 Call, J. & Tomasello, M. 2008 Does the chimpanzee
have a theory of mind? 30 years later. Trends Cogn.
Sci. 12, 187 –192. (doi:10.1016/j.tics.2008.02.010)
19 Katz, P. S. 2011 Neural mechanisms underlying the
evolvability of behaviour. Phil. Trans. R. Soc. B 366,
2086 –2099. (doi:10.1098/rstb.2010.0336)
20 Liu, Y., Rossiter, S. J., Han, X. Q., Cotton, J. A. &
Zhang, S. Y. 2010 Cetaceans on a molecular fast track
to ultrasonic hearing. Curr. Biol. 20, 1834–1839.
(doi:10.1016/j.cub.2010.09.008)
21 Liu, Y., Cotton, J. A., Shen, B., Han, X. Q.,
Rossiter, S. J. & Zhang, S. Y. 2010 Convergent
sequence evolution between echolocating bats and dolphins. Curr. Biol. 20, R53–R54. (doi:10.1016/j.cub.
2009.11.058)
22 Emery, N. J. & Clayton, N. S. 2004 The mentality of
crows: convergent evolution of intelligence in corvids
and apes. Science 306, 1903–1907. (doi:10.1126/
science.1098410)
23 Kawasaki, M. 2009 Evolution of time-coding systems in
weakly electric fishes. Zool. Sci. 26, 587 –599. (doi:10.
2108/zsj.26.587)
Phil. Trans. R. Soc. B (2012)
L. Chittka et al.
2683
24 Healy, S. D. & Rowe, C. 2007 A critique of comparative
studies of brain size. Proc. R. Soc. B 274, 453– 464.
(doi:10.1098/rspb.2006.3748)
25 Striedter, G. 2005 Principles of brain evolution.
Sunderland, MA: Sinauer.
26 Strausfeld, N. J. 2009 Brain organization and the origin
of insects: an assessment. Proc. R. Soc. B 276, 1929–
1937. (doi:10.1098/rspb.2008.1471)
27 Padberg, J., Franca, J. G., Cooke, D. F., Soares,
J. G. M., Rosa, M. G. P., Fiorani, M., Gattass, R. &
Krubitzer, L. 2007 Parallel evolution of cortical areas
involved in skilled hand use. J. Neurosci. 27, 10 106–
10 115. (doi:10.1523/jneurosci.2632-07.2007)
28 Lazure, L. & Fenton, M. B. 2011 High duty cycle echolocation and prey detection by bats. J. Exp. Biol. 214,
1131–1137. (doi:10.1242/jeb.048967)
29 Schnitzler, H.-U. & Denzinger, A. 2011 Auditory fovea
and Doppler shift compensation: adaptations for flutter
detection in echolocating bats using CF-FM signals.
J. Comp. Physiol. 197, 541–559. (doi:10.1007/s00359010-0569-6)
30 Sheehan, M. J. & Tibbetts, E. A. 2008 Robust longterm social memories in a paper wasp. Curr. Biol. 18,
R851–R852. (doi:10.1016/j.cub.2008.07.032)
31 Gronenberg, W., Ash, L. E. & Tibbetts, E. A. 2008
Correlation between facial pattern recognition and
brain composition in paper wasps. Brain Behav. Evol.
71, 1 –14. (doi:10.1159/000108607)
32 Brockmann, A. & Robinson, G. E. 2007 Central projections of sensory systems involved in honey bee
dance language communication. Brain Behav. Evol.
70, 125 –136. (doi:10.1159/000102974)
33 Holloway, R. L. 2002 Brief communication: how much
larger is the relative volume of area 10 of the prefrontal
cortex in humans? Am. J. Phys. Anthropol. 118,
399–401. (doi:10.1002/ajpa.10090)
34 Petrides, M., Cadoret, G. V. & Mackey, S. 2005 Orofacial somatomotor responses in the macaque monkey
homologue of Broca’s area. Nature 435, 1235–1238.
(doi:10.1038/nature03628)
35 Spocter, M. A., Hopkins, W. D., Garrison, A. R.,
Bauernfeind, A. L., Stimpson, C. D., Hof, P. R. &
Sherwood, C. C. 2010 Wernicke’s area homologue in
chimpanzees (Pan troglodytes) and its relation to the
appearance of modern human language. Proc. R. Soc.
B 277, 2165– 2174. (doi:10.1098/rspb.2010.0011)
36 Gallistel, C. R. & King, A. P. 2009 Memory and the computational brain: why cognitive science will transform
neuroscience. Oxford, UK: Wiley-Blackwell.
37 Menzel, R. & Giurfa, M. 2001 Cognitive architecture of
a mini-brain: the honeybee. Trends Cogn. Sci. 5, 62–71.
(doi:10.1016/S1364-6613(00)01601-6)
38 Webb, B. 2012 Cognition in insects. Phil. Trans. R. Soc.
B 367, 2715– 2722. (doi:10.1098/rstb.2012.0218)
39 Niven, J. E. & Burrows, M. 2003 Spike width reduction
modifies the dynamics of short-term depression at a central synapse in the locust. J. Neurosci. 23, 7461–7469.
40 Cassenaer, S. & Laurent, G. 2007 Hebbian STDP in
mushroom bodies facilitates the synchronous flow of
olfactory information in locusts. Nature 448, 709 –712.
(doi:10.1038/nature05973)
41 Burrows, M. & Laurent, G. 1993 Synaptic potentials in
the central terminals of locust proprioceptive afferents
generated by other afferents from the same sense
organ. J. Neurosci. 13, 808 –819.
42 Burrows, M. 1996 The neurobiology of an insect brain.
Oxford, UK: Oxford University Press.
43 Chittka, L. 1996 Does bee colour vision predate the
evolution of flower colour? Naturwissenschaften 83,
136–138. (doi:10.1007/BF01142181)
Downloaded from rstb.royalsocietypublishing.org on September 7, 2012
2684
L. Chittka et al.
Review. Cognition evolution
44 Frentiu, F. D., Bernard, G. D., Sison-Mangus, M. P.,
Brower, A. V. Z. & Briscoe, A. D. 2007 Gene duplication is an evolutionary mechanism for expanding
spectral diversity in the long-wavelength photopigments
of butterflies. Mol. Biol. Evol. 24, 2016–2028. (doi:10.
1093/molbev/msm132)
45 Yokoyama, S. & Yokoyama, R. 1996 Adaptive evolution
of photoreceptors and visual pigments in vertebrates.
Annu. Rev. Ecol. Syst. 27, 543– 567. (doi:10.1146/
annurev.ecolsys.27.1.543)
46 Hunt, D. M., Carvalho, L. S., Cowing, J. A. & Davies,
W. L. 2009 Evolution and spectral tuning of visual pigments in birds and mammals. Phil. Trans. R. Soc. 364,
2941–2955. (doi:10.1098/rstb.2009.0044)
47 Zhao, H., Rossiter, S. J., Teeling, E. C., Li, C., Cotton,
J. A. & Zhang, S. 2009 The evolution of color vision in
nocturnal mammals. Proc. Natl Acad. Sci. USA 106,
8980–8985. (doi:10.1073/pnas.0813201106)
48 Jacobs, G. H., Williams, G. A., Cahill, H. & Nathans, J.
2007 Emergence of novel color vision in mice engineered to express a human cone photopigment. Science
315, 1723–1725. (doi:10.1126/science.1138838)
49 Skorupski, P. & Chittka, L. 2011 Is colour cognitive?
Optics Laser Technol. 43, 251 –260. (doi:10.1016/j.
optlastec.2008.12.015)
50 Wolf, R., Voss, A., Hein, S. & Heisenberg, M. 1992
Can a fly ride a bicycle? Phil. Trans. R. Soc. Lond. B
337, 261 –269. (doi:10.1098/rstb.1992.0104)
51 Shou, C., Bhardwaj, N., Lam, H. Y. K., Yan, K.-K.,
Kim, P. M., Snyder, M. & Gerstein, M. B. 2011
Measuring the evolutionary rewiring of biological networks. PLoS Comput. Biol. 7, e1001050. (doi:10.1371/
journal.pcbi.1001050)
52 Li, G., Wang, J., Rossiter, S. J., Jones, G., Cottont,
J. A. & Zhang, S. 2008 The hearing gene Prestin
reunites echolocating bats. Proc. Natl Acad. Sci. USA
105, 13 959 –13 964. (doi:10.1073/pnas.0802097105)
53 Davies, K., Cotton, J., Kirwan, J., Teeling, E. &
Rossiter, S. J. 2011 Parallel signatures of sequence evolution among hearing genes in echolocating mammals:
an emerging model of genetic convergence. Heredity
108, 480 –489. (doi:10.1038/hdy.2011.119)
54 Fisher, S. E. & Scharff, C. 2009 FOXP2 as a molecular
window into speech and language. Trends Genet. 25,
166 –177. (doi:10.1016/j.tig.2009.03.002)
55 Enard, W. 2011 FOXP2 and the role of cortico-basal
ganglia circuits in speech and language evolution.
Curr. Opin. Neurobiol. 21, 415 –424. (doi:10.1016/j.
conb.2011.04.008)
56 Enard, W., Przeworski, M., Fisher, S. E., Lai, C. S. L.,
Wiebe, V., Kitano, T., Monaco, A. P. & Paabo, S. 2002
Molecular evolution of FOXP2, a gene involved in
speech and language. Nature 418, 869 –872. (doi:10.
1038/nature01025)
57 Vernes, S. C., Spiteri, E., Nicod, J., Groszerm, M.,
Taylor, J. M., Davies, K. E., Geschwind, D. H. &
Fisher, S. E. 2007 High-throughput analysis of
promoter occupancy reveals direct neural targets of
FOXP2, a gene mutated in speech and language disorders. Am. J. Hum. Genet. 81, 1232–1250. (doi:10.
1086/522238)
58 Reimers-Kipping, S., Hevers, W., Paeaebo, S. &
Enard, W. 2011 Humanized Foxp2 specifically affects
cortico-basal ganglia circuits. Neuroscience 175, 75– 84.
(doi:10.1016/j.neuroscience.2010.11.042)
59 Haesler, S., Wada, K., Nshdejan, A., Morrisey, E. E.,
Lints, T., Jarvis, E. D. & Scharff, C. 2004 FoxP2
expression in avian vocal learners and non-learners.
J. Neurosci. 24, 3164–3175. (doi:10.1523/jneurosci.
4369-03.2004)
Phil. Trans. R. Soc. B (2012)
60 Haesler, S., Rochefort, C., Georgi, B., Licznerski, P.,
Osten, P. & Scharff, C. 2007 Incomplete and inaccurate
vocal imitation after knockdown of FoxP2 in songbird
basal ganglia nucleus area X. PLoS Biol. 5, e321.
(doi:10.1371/journal.pbio.0050321)
61 Bolhuis, J. J., Okanoya, K. & Scharff, C. 2010 Twitter
evolution: converging mechanisms in birdsong and
human speech. Nat. Rev. Neurosci. 11, 747 –759.
(doi:10.1038/nrn2931)
62 Li, G., Wang, J., Rossiter, S. J., Jones, G. & Zhang, S.
2007 Accelerated FoxP2 evolution in echolocating
bats. PLoS ONE 2, e900. (doi:10.1371/journal.pone.
0000900)
63 Metzner, W. & Schuller, G. 2010 Vocal control in echolocating bats. In Handbook of mammalian vocalizations
(ed. S. Brudzynski), pp. 403 –415. Amsterdam, The
Netherlands: Elsevier.
64 Paul, D. H. 2007 Evolutionary neuroethology: a case
study: origin and evolution of novel forms of locomotion in Hippid sand crabs. In Evolution of nervous
systems (ed. J. H. Kaas), pp. 99– 119. Oxford, UK:
Academic Press.
65 Ocker, W. & Hedwig, B. 1996 Interneurones involved
in stridulatory pattern generation in the grasshopper Chorthippus mollis (Charp.). J. Exp. Biol. 199,
653 –662.
66 Thornton, A. & Lukas, D. 2012 Individual variation in
cognitive performance: developmental and evolutionary
perspectives. Phil. Trans. R. Soc. B 367, 2773 –2783.
(doi:10.1098/rstb.2012.0214)
67 Herrmann, E. & Call, J. 2012 Are there geniuses among
the apes? Phil. Trans. R. Soc. B 367, 2753 –2761.
(doi:10.1098/rstb.2012.0191)
68 Chittka, L. & Thomson, J. D. 1997 Sensori-motor
learning and its relevance for task specialization in
bumble bees. Behav. Ecol. Sociobiol. 41, 385–398.
(doi:10.1007/s002650050400)
69 Chittka, L. & Reinhold, H. 1999 Towards an individual-based approach to insect learning. In Proc. 27th
Go¨ttingen Neurobiology Conf. (eds N. Elsner &
U. Eysel), p. 257. Berlin, Germany: Thieme.
70 Raine, N. E., Ings, T. C., Dornhaus, A., Saleh, N. &
Chittka, L. 2006 Adaptation, genetic drift, pleiotropy,
and history in the evolution of bee foraging behavior.
Ad. Study Behav. 36, 305–354. (doi:10.1016/S00653454(06)36007-X)
71 Page, R. E. J., Schneir, R., Erber, J. & Amdam, G. V.
2006 The development and evolution of division of
labor and foraging specialization in a social insect
(Apis mellifera L.). Curr. Top. Dev. Biol. 74, 253 –286.
(doi:10.1016/S0070-2153(06)74008-X)
72 Raine, N. E. & Chittka, L. 2008 The correlation of
learning speed and natural foraging success in
bumble-bees. Proc. R. Soc. B 275, 803–808. (doi:10.
1098/rspb.2007.1652)
73 Brandes, C. 1987 Effects of bidirectional selection on
learning behavior in honeybees (Apis mellifera capensis).
In Chemistry and biology of social insects (eds J. Eder &
H. Rembold), pp. 192 –193. Muenchen, Germany: J.
Peperny.
74 Lofdahl, K. L., Holliday, M. & Hirsch, J. 1992 Selection for conditionability in Drosophila melanogaster.
J. Comp. Psychol. 106, 172 –183. (doi:10.1037/07357036.106.2.172)
75 Endler, J. A., Basolo, A., Glowacki, S. & Zerr, J. 2001
Variation in response to artificial selection for light sensitivity in guppies (Poecilia reticulata). Am. Nat. 158,
36– 48. (doi:10.1086/320862)
76 Darwin, C. 1871 The descent of man, and selection in
relation to sex. London, UK: John Murray.
Downloaded from rstb.royalsocietypublishing.org on September 7, 2012
Review. Cognition evolution
77 Robbins, T. W. & Kousta, S. 2011 Uncovering the genetic underpinnings of cognition. Trends Cogn. Sci. 15,
375 –377. (doi:10.1016/j.tics.2011.07.006)
78 Bouchard, T. J. & McGue, M. 2003 Genetic and
environmental influences on human psychological
differences. J. Neurobiol. 54, 4 –45. (doi:10.1002/
neu.10160)
79 Chater, N. 1999 The search for simplicity: a fundamental cognitive principle? Q. J. Exp. Psychol. A 52,
273 –302. (doi:10.1080/027249899391070)
80 Chittka, L., Kunze, J., Shipman, C. & Buchmann, S. L.
1995 The significance of landmarks for path integration
of homing honey bee foragers. Naturwiss 82, 341–343.
(doi:10.1007/BF01131533)
81 Cruse, H. & Wehner, R. 2011 No need for a cognitive
map: decentralized memory for insect navigation.
PLoS Comput. Biol. 7, e1002009. EP.
82 Beer, R. D. 2003 The dynamics of active categorical
perception in an evolved model agent (with commentary and response). Adapt. Behav. 11, 209–243.
(doi:10.1177/1059712303114001)
83 Williams, P. L., Beer, R. D. & Gasser, M. 2008
An embodied dynamical approach to relational
categorization. In Proc. 30th Annual Conf. Cognitive
Science Society, Washington DC, 23 – 28 July 2008
(eds B. C. Love, K. McRae & V. M. Sloutsky),
pp. 223 –228.
84 Soltoggio, A., Du¨rr, P., Mattiussi, C. & Floreano, D.
2007 Evolving neuromodulatory topologies for
reinforcement learning-like problems. In Proc. 2007
IEEE Congress on Evolutionary Computation, Singapore,
25 –28 September 2007, pp. 2471–2478. (doi:10.1109/
CEC.2007.4424781)
85 Niv, Y., Joel, D., Meilijson, I. & Rupping, E. 2002
Evolution of reinforcement learning in uncertain
environments: a simple explanation for complex foraging behaviors. Adapt. Behav 10, 5 –23. (doi:10.1177/
10597123020101001)
86 Floreano, D. & Keller, L. 2010 Evolution of adaptive behaviour in robots by means of darwinian
selection. PLoS Biol. 8, e1000292. (doi:10.1371/
journal.pbio.1000292)
87 Dayan, P. & Niv, Y. 2008 Reinforcement learning: the
good, the bad and the ugly. Curr. Opin. Neurobiol. 18,
185 –196. (doi:10.1016/j.conb.2008.08.003)
88 Branson, K., Robie, A. A., Bender, J., Perona, P. &
Dickinson, M. H. 2009 High-throughput ethomics
in large groups of Drosophila. Nat. Methods 6,
U451 –U477. (doi:10.1038/nmeth.1328)
89 Benjamini, Y., Fonio, E., Galili, T., Havkin, G. Z. &
Golani, I. 2011 Quantifying the buildup in extent and
complexity of free exploration in mice. Proc. Natl
Acad. Sci. 108(Suppl. 3) 15 580 –15 587. (doi:10.
1073/pnas.1014837108)
90 Braun, E., Geurten, B. & Egelhaaf, M. 2010 Identifying
prototypical components in behaviour using clustering
algorithms. PLoS ONE 5, e9361. (doi:10.1371/journal.
pone.0009361)
Phil. Trans. R. Soc. B (2012)
L. Chittka et al.
2685
91 Maggio, E. & Cavallaro, A. 2011 Video tracking: theory
and practice. Chichester, UK: John Wiley & Sons.
92 Anjum, N. & Cavallaro, A. 2008 Multi-feature object
trajectory clustering for video analysis. IEEE Trans. Circuits Syst. Video Technol. 18, 1555–1564. (doi:10.1109/
TCSVT.2008.2005603)
93 Chittka, L. 1998 Sensorimotor learning in bumblebees:
long term retention and reversal training. J. Exp. Biol.
201, 515–524.
94 Gould, J. L. & Marler, P. 1984 Ethology and the natural
history of learning. In The biology of learning (eds
P. Marler & H. S. Terrace), pp. 47–74. Berlin,
Germany: Springer.
95 Wilson, M. A. & McNaughton, B. L. 1994 Reactivation
of hippocampal ensemble memories during sleep.
Science 265, 676 –679. (doi:10.1126/science.8036517)
96 McNaughton, B. L. et al. 1996 Deciphering the hippocampal polyglot: the hippocampus as a path integration
system. J. Exp. Biol. 199, 173– 185.
97 Chiang, A.-S. et al. 2010 Three-dimensional reconstruction of brain-wide wiring networks in Drosophila
at single-cell resolution. Curr. Biol. 21, 1–11. (doi:10.
1016/j.cub.2010.11.056)
98 Dodt, H. U. et al. 2007 Ultramicroscopy: threedimensional visualization of neuronal networks in the
whole mouse brain. Nat. Methods 4, 331– 336.
(doi:10.1038/nmeth1036)
99 Jahrling, N., Becker, K., Schonbauer, C., Schnorrer, F. &
Dodt, H. U. 2010 Three-dimensional reconstruction and
segmentation of intact Drosophila by ultramicroscopy.
Front. Syst. Neurosci. 4, 1. (doi:10.3389/neuro.3306.
3001.2010)
100 Chittka, L. 1996 Optimal sets of colour receptors and
opponent processes for coding of natural objects in
insect vision. J. Theor. Biol. 181, 179 –196. (doi:10.
1006/jtbi.1996.0124)
101 Wessnitzer, J., Young, J. M., Armstrong, J. D. & Webb,
B. 2012 A model of non-elemental olfactory learning in
Drosophila. J. Comput. Neurosci. 32, 197 –212. (doi:10.
1007/s10827-011-0348-6)
102 Metzker, M. L. 2010 Applications of next-generation
sequencing technologies: the next generation. Nat.
Rev. Genet. 11, 31–46. (doi:10.1038/nrg2626)
103 Davis, O. S. P., Butcher, L. M., Docherty, S. J.,
Meaburn, E. L., Curtis, C. J. C., Simpson, M. A.,
Schalkwyk, L. C. & Plomin, R. 2010 A three-stage
genome-wide association study of general cognitive
ability: hunting the small effects. Behav. Genet. 40,
759 –767. (doi:10.1007/s10519-010-9350-4)
104 Kunstner, A. et al. 2010 Comparative genomics based
on massive parallel transcriptome sequencing reveals
patterns of substitution and selection across 10 bird
species. Mol. Ecol. 19, 266 –276. (doi:10.1111/j.1365294X.2009.04487.x)
105 Wood, N. I., Glynn, D. & Morton, A. J. 2011 ‘Brain training’ improves cognitive performance and survival in a
transgenic mouse model of Huntington’s disease. Neurobiol. Dis. 42, 427–437. (doi:10.1016/j.nbd.2011.02.005)